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Vibe coding with your mouth is 3 times faster than typing. Now you need to describe a large number of requirements to AI every day. Providing context, constraints, and expected behavior for a feature takes about 5 minutes to type out. Yesterday, I tried Typeless, which allows you to directly speak your requirements to the computer, and it automatically organizes them into clear, structured text — not just raw speech transcription, but genuinely helping you sort out the logic, removing filler words, and segmenting where needed. The actual experience: a 200-word requirement description can be sp

Patching Claude Code's memory system Last week asked it to help me fix a memory leak bug, and this week I encountered a similar problem, and it guessed it from scratch. It can store preferences and project information, but it is not suitable for storing pit records - it is getting longer and longer, and it can only be searched in full text, and the search for "script keeps rising" cannot find "how to fix memory overflow". What I want is that when I have a problem, it can say "We solved something similar before." My solution: Maintain the experience library separately + semantic search + automa

AI coding has 5 levels. Which one are you at? This framework comes from Glowforge CEO Dan Shapiro, inspired by the levels of autonomous driving (see diagram). The recently popular Clawdbot (now renamed Moltbot) creator says he's at L5. With 60,000 stars on GitHub in 10 days, his approach includes: • Opening 4 Claude windows simultaneously to develop different features in parallel • "I don't design the code structure for myself, I design it for AI" • Never rolling back when issues occur, letting AI change direction core shift: from "code review" to "reviewing requirements + looking at test resu

Building a prediction market copy trading strategy, $200 → $52 lessons learned
I developed a bot that tracks whale trades on Polymarket. Paper Trading simulated over 5000 trades, with a 58% win rate and a profit of +$770. Confidently, I put in real money of $200.
Result: 25 real trades, with a 28% win rate, remaining $52.
Pitfalls encountered:
1. Phantom Decisions - The code recorded "I want to buy," but API errors prevented the trade from executing. The database stored a bunch of decision records, but the account balance remained unchanged. Self-deceptive data = false confidence.
2. Repeate

Vibe Coding Security Defense Record
Did a security audit for myself and found that more than 10 scripts in the project hardcoded wallet private keys—almost exposed funds on GitHub.
The cause was the IDE vulnerability warned by @evilcos, combined with previous reminders from friends during Polymarket data scraping.
🚨 The most dangerous scenario
Clone a project on GitHub, open it with Cursor—and the private keys are gone.
A configuration file is hidden in the project, and when the IDE opens it, commands are executed automatically without your knowledge. Cursor's official explanation is that thi

I'm too bad at this
I've been researching the Polymarket data project for over a week, constantly vibing and coding my own scraping scripts.
Today I just discovered:
1. poly_data has ready-made historical snapshots (free)
2. Polymarket Analytics offers comprehensive trader analysis (free)
3. The open-source community has already built the infrastructure
Lesson: Before reinventing the wheel, let Claude search GitHub.
Not all problems need to be solved from scratch. 😆

Is it too much to ask GPT to draw a picture describing our relationship🤣

Grok is one of the more user-friendly LLMs I've experienced for prediction markets because it can search the latest X messages in real-time and has comprehensive capabilities, often helping to analyze events.
However, sometimes it's quite absurd. Just now, it said a certain market had a huge edge, and a few minutes later, based on Monte Carlo simulations, it claimed the pricing was reasonable.
Why is using LLMs for predictions unreliable?
Lack of memory and feedback loop — LLMs don't remember what they've said before, always providing one-off answers.
Good at narrative pollution, bad at probab